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YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
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when I train yolov4 there is all 0,but yolov4-tiny works well. #6915

Open ChenCong7375 opened 4 years ago

ChenCong7375 commented 4 years ago

cuda10.0,cudnn7.4,opencv4.0.

the cfg file is the same as yolov4.cfg and I use yolov4.conv137.

ChenCong7375 commented 4 years ago

[net]

Testing

batch=1

subdivisions=1

Training

batch=64 subdivisions=64 width=608 height=608 channels=3 momentum=0.949 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1

learning_rate=0.00261 burn_in=1000 max_batches = 50050 policy=steps steps=40000,45000 scales=.1,.1

cutmix=1

mosaic=1

:104x104 54:52x52 85:26x26 104:13x13 for 416

[convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=mish

Downsample

[convolutional] batch_normalize=1 filters=64 size=3 stride=2 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[route] layers = -2

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=32 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[route] layers = -1,-7

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

Downsample

[convolutional] batch_normalize=1 filters=128 size=3 stride=2 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[route] layers = -2

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish

[route] layers = -1,-10

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

Downsample

[convolutional] batch_normalize=1 filters=256 size=3 stride=2 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[route] layers = -2

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish

[route] layers = -1,-28

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

Downsample

[convolutional] batch_normalize=1 filters=512 size=3 stride=2 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[route] layers = -2

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish

[route] layers = -1,-28

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

Downsample

[convolutional] batch_normalize=1 filters=1024 size=3 stride=2 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

[route] layers = -2

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish

[shortcut] from=-3 activation=linear

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish

[route] layers = -1,-16

[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=mish stopbackward=800

##########################

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky

SPP

[maxpool] stride=1 size=5

[route] layers=-2

[maxpool] stride=1 size=9

[route] layers=-4

[maxpool] stride=1 size=13

[route] layers=-1,-3,-5,-6

End SPP

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[upsample] stride=2

[route] layers = 85

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[route] layers = -1, -3

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky

[upsample] stride=2

[route] layers = 54

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky

[route] layers = -1, -3

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=256 activation=leaky

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=256 activation=leaky

[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=leaky

##########################

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=256 activation=leaky

[convolutional] size=1 stride=1 pad=1 filters=21 activation=linear

[yolo] mask = 0,1,2 anchors = 8,11, 8,16, 8,23, 8,33, 8,48, 8,97, 8,139, 8,198, 8,283 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 scale_x_y = 1.2 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 max_delta=5

[route] layers = -4

[convolutional] batch_normalize=1 size=3 stride=2 pad=1 filters=256 activation=leaky

[route] layers = -1, -16

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky

[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=512 activation=leaky

[convolutional] size=1 stride=1 pad=1 filters=21 activation=linear

[yolo] mask = 3,4,5 anchors = 8,11, 8,16, 8,23, 8,33, 8,48, 8,97, 8,139, 8,198, 8,283 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 scale_x_y = 1.1 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 max_delta=5

[route] layers = -4

[convolutional] batch_normalize=1 size=3 stride=2 pad=1 filters=512 activation=leaky

[route] layers = -1, -37

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky

[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky

[convolutional] size=1 stride=1 pad=1 filters=21 activation=linear

[yolo] mask = 6,7,8 anchors = 8,11, 8,16, 8,23, 8,33, 8,48, 8,97, 8,139, 8,198, 8,283 classes=2 num=9 jitter=.3 ignore_thresh = .7 truth_thresh = 1 random=1 scale_x_y = 1.05 iou_thresh=0.213 cls_normalizer=1.0 iou_normalizer=0.07 iou_loss=ciou nms_kind=greedynms beta_nms=0.6 max_delta=5

ChenCong7375 commented 4 years ago

when about 1000 iterations ,IOU,GIOU,Obj turn to 0.

bulatnv commented 4 years ago

Hello. check your dateset? then try to decrease network width, height, batch_size and subdivision. width = 416 height = 416 batch_size = 32 subdidvision = 16

And could you, please, next time write only the lines from cfg that you have changed.

ChenCong7375 commented 4 years ago

Hello. check your dateset? then try to decrease network width, height, batch_size and subdivision. width = 416 height = 416 batch_size = 32 subdidvision = 16

And could you, please, next time write only the lines from cfg that you have changed.

OK I'll have a try.thx.

ChenCong7375 commented 4 years ago

Hello. check your dateset? then try to decrease network width, height, batch_size and subdivision. width = 416 height = 416 batch_size = 32 subdidvision = 16

And could you, please, next time write only the lines from cfg that you have changed.

there is useless. I checked my dataset and changed cfg file.but tiny still works well.

bulatnv commented 4 years ago

@ChenCong7375,

What hardware do you use?

And can you tell more about training (2 classes, how many images per class for training and validation, image sizes and other) I'm also struggling to train yolov4. (Sometimes results are good and I don't understand why).

My configurations: Core i7, 32GB, 2080Super (8GB GRAM), W10, CUDA 10.1, cuDNN 7.6.5.

I'm training for 46 classes (imbalanced badly).

First attempt:

bigdataset ~100K images

batch=64
subdivisions=64
width=608
height=608
classes=46
filters=153

mAP was about 64%

Second attempt:

unsampled dataset ~= 27K images for train and 9K images for validation. (not balanced, but much better than it was before, at least 2K times every class appear in train)

batch=64
subdivisions=64
width=608
height=608
classes=46
filters=153

mAP about 83%

Third attempt:

Additional training for second on bigger dataset (100K images) configuration is the same, results => mAP about 33% ((:

Probably: 1) Try to use as small train set as possible, but at least 2K images per class (you will train faster and check different training configurations) 2) Try to increase mini-batch size, as big as possible (for this reason I told you to decrease width, height, batch-size and subdivision) 3) And who knows, maybe there are other problems (update software(CUDA + cuDNN)) 4) Have look here: #4832, #4671

Good luck.